{"_buckets": {"deposit": "ea3eca98-475e-4e02-bb39-1eb3dee224d2"}, "_deposit": {"id": "8565", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "8565"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00008565"}, "item_10_alternative_title_19": {"attribute_name": "\u305d\u306e\u4ed6\u306e\u8a00\u8a9e\u306e\u30bf\u30a4\u30c8\u30eb", "attribute_value_mlt": [{"subitem_alternative_title": "A Branch Prediction Scheme that Reduces Destructive Aliasing Using Branch Direction Bias"}]}, "item_10_biblio_info_6": {"attribute_name": "\u66f8\u8a8c\u60c5\u5831", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "1999-05", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "5", "bibliographicPageEnd": "2131", "bibliographicPageStart": "2119", "bibliographicVolumeNumber": "40", "bibliographic_titles": [{"bibliographic_title": "\u60c5\u5831\u51e6\u7406\u5b66\u4f1a\u8ad6\u6587\u8a8c"}]}]}, "item_10_description_4": {"attribute_name": "\u6284\u9332", "attribute_value_mlt": [{"subitem_description": "\u547d\u4ee4\u30ec\u30d9\u30eb\u4e26\u5217\u6027\u3092\u5229\u7528\u3059\u308b\u9ad8\u6027\u80fd\u30d7\u30ed\u30bb\u30c3\u30b5\u306b\u304a\u3044\u3066\u306f, \u5206\u5c90\u4e88\u6e2c\u6a5f\u69cb\u306e\u4e88\u6e2c\u7cbe\u5ea6\u306f\u6027\u80fd\u306b\u5927\u304d\u306a\u5f71\u97ff\u3092\u4e0e\u3048\u308b. \u5206\u5c90\u4e88\u7cbe\u5ea6\u3092\u5236\u9650\u3059\u308b\u4e3b\u305f\u308b\u8981\u56e0\u306f, \u7af6\u5408\u3067\u3042\u308b\u3053\u3068\u304c\u77e5\u3089\u308c\u3066\u3044\u308b. \u3053\u308c\u307e\u3067\u7af6\u5408\u3078\u306e\u5bfe\u7b56\u3068\u3057\u3066, \u305d\u306e\u767a\u751f\u305d\u306e\u3082\u306e\u3092\u6e1b\u5c11\u3055\u305b\u308b\u8a66\u307f\u304c\u306a\u3055\u308c\u3066\u304d\u305f. \u3057\u304b\u3057\u3053\u306e\u65b9\u7b56\u3060\u3051\u3067\u306f\u4e88\u6e2c\u7cbe\u5ea6\u3092\u6539\u5584\u3059\u308b\u306b\u306f\u9650\u754c\u304c\u3042\u308b. \u3053\u308c\u306b\u5bfe\u3057\u3066\u6211\u3005\u306f, \u7af6\u5408\u306e\u4e2d\u3067\u3082\u4e88\u6e2c\u306b\u60aa\u5f71\u97ff\u3092\u4e0e\u3048\u308b\u7834\u58ca\u7684\u306a\u7af6\u5408\u306e\u524a\u6e1b\u306b\u6ce8\u76ee\u3057, \u7af6\u5408\u304c\u767a\u751f\u3057\u3066\u3082\u4e88\u6e2c\u7cbe\u5ea6\u3092\u5927\u304d\u304f\u4f4e\u4e0b\u3055\u305b\u306a\u3044\u30a2\u30d7\u30ed\u30fc\u30c1\u3092\u3068\u308b. \u3053\u306e\u30a2\u30d7\u30ed\u30fc\u30c1\u306b\u57fa\u3065\u304dsgshare\u3068\u547c\u3076\u65b0\u3057\u3044\u4e88\u6e2c\u6a5f\u69cb\u3092\u63d0\u6848\u3059\u308b. sgshare\u3067\u306f, \u30d1\u30bf\u30fc\u30f3\u5c65\u6b74\u30c6\u30fc\u30d6\u30eb\u30922\u3064\u306b\u5206\u96e2\u3057, \u5206\u5c90\u65b9\u5411\u306e\u504f\u308a\u304c\u540c\u4e00\u306e\u5206\u5c90\u3092\u4e00\u65b9\u306e\u30c6\u30fc\u30d6\u30eb\u306b\u306e\u307f\u30de\u30c3\u30d4\u30f3\u30b0\u3059\u308b\u3053\u3068\u306b\u3088\u308a, \u7834\u58ca\u7684\u306a\u7af6\u5408\u3092\u7121\u5bb3\u306a\u7af6\u5408\u306b\u5909\u63db\u3059\u308b. IBS-Ultrix\u30d9\u30f3\u30c1\u30de\u30fc\u30af\u3092\u7528\u3044\u305f\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u306b\u3088\u308a\u8a55\u4fa1\u3092\u3057\u305f\u7d50\u679c, sgshare\u306f\u5f93\u6765\u306egshare\u3068\u6bd4\u8f03\u3057\u3066, \u7d048K\u30d0\u30a4\u30c8\u306e\u30cf\u30fc\u30c9\u30a6\u30a7\u30a2\u30fb\u30b3\u30b9\u30c8\u306b\u304a\u3044\u3066, 0.05\uff5e0.89%\u4e88\u6e2c\u7cbe\u5ea6\u304c\u5411\u4e0a\u3059\u308b\u3053\u3068\u3092\u78ba\u8a8d\u3057\u305f. \u307e\u305f, \u3053\u306e\u4e88\u6e2c\u7cbe\u5ea6\u306e\u6539\u5584\u306b\u3088\u308a, \u30d7\u30ed\u30bb\u30c3\u30b5\u306e\u6027\u80fd\u306f\u4eca\u65e5\u306e\u30b9\u30fc\u30d1\u30fc\u30b9\u30ab\u30e9\u30fb\u30de\u30b7\u30f3\u306b\u304a\u3044\u3066\u5e73\u57477.19%, \u3088\u308a\u5e83\u3044\u547d\u4ee4\u767a\u884c\u5e45\u3068\u3088\u308a\u6df1\u3044\u30d1\u30a4\u30d7\u30e9\u30a4\u30f3\u3092\u6301\u3064\u5c06\u6765\u306e\u30b9\u30fc\u30d1\u30fc\u30b9\u30ab\u30e9\u30fb\u30de\u30b7\u30f3\u306b\u304a\u3044\u3066\u5e73\u574714.6%\u901f\u5ea6\u304c\u5411\u4e0a\u3059\u308b\u3053\u3068\u3092\u78ba\u8a8d\u3057\u305f. Branch prediction accuracy makes a tremendous impact on modern high-performance microprocessors that exploit instruction-level parallelism. Aliasing is a known major factor that limits prediction accuracy. A several studies have attempted to reduce the total rate of aliasing. Yet, only this approach is not enough for further improvement of prediction accuracy. In this paper, we focus on reducing destructive aliasing that has a negative impact on prediction accuracy. We propose a new branch prediction scheme, called sgshare, which modestly affects prediction accuracy even when aliasing occurs. Sgshare divides the pattern history table into two parts, and maps branches with the same direction bias to only a single part of the table. This mechanism converts destructive aliasing into harmless aliasing. Our evaluation results show that sgshare achieves 0.05-0.89% better prediction accuracy than the conventional gshare with approximately 8 K-byte hardware cost. 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